S265: RADIOMICS AND ARTIFICIAL INTELLIGENCE FOR IDENTIFICATION AND MONITORING OF SILENT CEREBRAL INFARCTS IN SICKLE CELL DISEASE: FIRST ANALYSIS FROM THE GENOMED4ALL EUROPEAN PROJECT
Authors: M. P. Boaro, R. Biondi, N. Biondini, A. Collado Gimbert, E. F. JM, V. Pinto, N. Romano, V. Voi, G. B. Ferrero, M. Casale, M. Cirillo, G. Palazzi, F. Cavalleri, G. L. Forni, G. Reggiani, S. Perrotta, M. Manu Pereira, S. Zazo, K. Marias, M. De Montalembert, P. Bartolucci, E. van Beers, F. Alvarez, F. Cremonesi, T. Sanavia, P. Fariselli, G. Castellani, R. Manara, R. Colombatti
Journal: HemaSphere (2022)

Background: The use of Artificial Intelligence (AI) for personalized medicine has recently guided improvements in the diagnostic pathway of many diseases. The EU Project GENOMED4ALL: “Genomics and Personalized Medicine for All through Artificial Intelligence in Haematological Diseases” aims at using European data of patients affected by Sickle Cell Disease (SCD) to find correlation between -omics data – and phenotype, seizing the opportunity to improve diagnostics through AI. Silent Cerebral Infarcts (SCIs) are a significant cause of morbidity in SCD: they affect 25% of children by the age of 6 and 40% by the age of 18 with consequences on cognition, schooling, working capacity and quality of life. Hence, one of the aims of the SCD clinical case in GENOMED4ALL is the use of radiomics – quantitative method for the evaluation and interpretation of medical images- and AI firstly to develop an automatic and uniform identification and characterization of SCI on MRIs, secondly, to correlate imaging data with other types of omics data in order to predict risk of recurrence Aims: The first phase of the GENOMED4ALL project aimed at collecting anonymized MRI data available in Centers of the EuroBloodNet Network to develop an algorithm that could identify SCI on MRI from different sources, distinguishing them from other lesions Methods: MRI protocol included 3D-T1, FLAIR and DWI sequences. Neuroradiological reports were checked for consistency. A stepwise segmentation (identification of lesion volume), pre-processing and extraction of MRIs was performed utilizing different open-source software: Lesion Segmentation Tools and UNet for segmentation, Freesurfer for brain extraction. To optimize SCI identification, the best software for segmentation was selected Results: Six SCD expert centers participated in the first phase with 501 MRIs: 225 were classified as abnormal by the local neuroradiologists due to presence of SCI; 70% were pediatric MRIs. Different instruments were used in the centers: Philips 1.5 T (n.2), Siemens 1.5 T and 3 T (n.2), GE 3T (n.2). A stepwise procedure allowed optimization of SCI identification, with distinction between SCI and non-clinically significant background (periventricular areas) or other white matter hyperintensities (transient glial maturation). As shown in Figure 1, to segment SCI in FLAIR MRI, we used a pre-trained UNet [Li H, 2018] winner of the MICCAI challenge. Firstly, we extracted the brain registering the MNI152 on the T1 image. Then we applied the brain mask and a threshold taking only the largest component. The UNet performed the segmentation of hyperintensities. We removed the regions surrounded by less than 90% of White Matter or near the brain ventricles to remove non-SCI region (refinement). We then enlarged the remaining ones. To date, segmentation on 303 exams from different centers showed very few false positive and some false negatives highlighting the need to take into account different technical characteristics (various equipment in different centers) Image:Summary/Conclusion: SCD is a rare systemic disorder with extreme phenotypic variability. Radiomics and AI offer the opportunity to seize the potential of big dataset analysis to understand natural history and optimize diagnostics. SCI can be detected automatically from different datasets. Four more European Centers will add their MRI in the second phase, in order to increase variability and allow correlation of detailed Radiomics features with clinical-hematological variables and other omics data